RL = PolicyGradient(
n_actions=env.n_actions,
n_features=env.n_features,
learning_rate=0.01,
reward_decay=0.99,
)
fid_max = 0
for episode in range(500):
observation = env.reset()
for ii in range(N):
action = RL.choose_action(observation)
observation_, reward, done, fid = env.step(action, ii)
RL.store_transition(observation, action, reward)
observation = observation_
if done or ii >= N - 1:
break
if episode >= 490:
if fid > fid_max:
fid_max = np.copy(fid)
RL.learn()
print('Final_fidelity=', fid_max)
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.99,
replace_target_iter=200,
memory_size=2000,
e_greedy_increment=0.001,
)
step = 0
fid_max = 0
for episode in range(500):
observation = env.reset()
for i in range(N):
action = RL.choose_action(observation)
observation_, reward, done, fidelity = env.step(action, i)
RL.store_transition(observation, action, reward, observation_)
if (step > 500) and (step % 5 == 0):
RL.learn()
observation = observation_
if done:
break
step += 1
if episode >= 490:
if fidelity > fid_max:
fid_max = np.copy(fidelity)